12 papers
PatchINR: Patch-Based Implicit Neural Representations for Efficient and Scalable Inference
Jiachen Ren, Wenyong Zhou, Taiqiang Wu +4
Implicit Neural Representation (INR) provides an effective approach for continuous signal modeling, but classical per-pixel inference results in quadratic growth in inference count…
Model Evolution Under Zeroth-Order Optimization: A Neural Tangent Kernel Perspective
Chen Zhang, Yuxin Cheng, Chenchen Ding +5
Zeroth-order (ZO) optimization enables memory-efficient training of neural networks by estimating gradients via forward passes only, eliminating the need for backpropagation. Howev…
Can We Trust LLMs on Memristors? Diving into Reasoning Ability under Non-Ideality
Taiqiang Wu, Yuxin Cheng, Chenchen Ding +5
Memristor-based analog compute-in-memory (CIM) architectures provide a promising substrate for the efficient deployment of Large Language Models (LLMs), owing to superior energy ef…
HaLoRA: Hardware-aware Low-Rank Adaptation for Large Language Models Based on Hybrid Compute-in-Memory Architecture
Taiqiang Wu, Chenchen Ding, Wenyong Zhou +7
Low-rank adaptation (LoRA) is a predominant parameter-efficient finetuning method for adapting large language models (LLMs) to downstream tasks. Meanwhile, Compute-in-Memory (CIM)…
InjectRBP: Steering Large Language Model Reasoning Behavior via Pattern Injection
Xiuping Wu, Zhao Yu, Yuxin Cheng +4
Reasoning can significantly enhance the performance of Large Language Models. While recent studies have exploited behavior-related prompts adjustment to enhance reasoning, these de…
OVD: On-policy Verbal Distillation
Jing Xiong, Hui Shen, Shansan Gong +7
Knowledge distillation offers a promising path to transfer reasoning capabilities from large teacher models to efficient student models; however, existing token-level on-policy dis…